Machine Learning Alert Triage for AML False Positive Reduction
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Solution Overview
Problem
Rule-based systems for anti-money laundering generate a high number of false positives, leading to inefficient and tedious alert review processes.
Innovation Solution
Implement a machine learning model to score and triage alerts, allowing for prioritization and queue assignment based on the likelihood of suspicious activities, with the option for human feedback and model re-training to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rule-based systems are used to analyze transactional data, then alerts can be generated for potentially suspicious transactions, but the false positive rate becomes high and alert review becomes tedious
Solution Approach 1:
A machine learning triage model is introduced as an intermediary between the rule-based alert generation system and human analysts. The model scores alerts to distinguish true positives from false positives, reducing the burden on analysts while maintaining reliable detection of suspicious transactions.
Solution Approach 2:
The manual mechanical review process is partially replaced with an automated machine learning-based scoring system. This substitution reduces the tedious nature of alert review by automatically prioritizing alerts that require human attention.
2Difficulty of detecting and measuring
If rule-based systems generate alerts for suspicious transactions, then potentially suspicious activities can be identified, but the large number of alerts makes review inefficient
Solution Approach 1:
The machine learning model performs preliminary triage and scoring of alerts before they reach human analysts. By pre-processing and prioritizing alerts based on their likelihood of being true positives, the system reduces the time analysts need to spend reviewing each alert.
Solution Approach 2:
Different alerts are treated differently based on their individual characteristics and scores. High-scoring alerts receive immediate analyst attention, while low-scoring alerts are deprioritized or automatically closed, optimizing the allocation of review time across different alert types.
3Reliability
If more rules are added to detect suspicious transactions, then detection coverage improves, but false positive rate increases
Solution Approach 1:
The machine learning model learns from feedback about alert outcomes to continuously improve its scoring accuracy. By incorporating feedback loops, the system adapts to reduce false positives while maintaining comprehensive detection coverage across different transaction patterns.
Data Source
AI summary
In various embodiment, a process for triaging alerts using machine learning includes receiving data associated with transactions and using computer processor(s) to analyze the received data using rule(s) to automatically identify potentially suspicious activities. The process includes scoring each of the identified potentially suspicious activities using a machine learning model and based at least in part on analysis results of the rule(s) associated with the identified potentially suspicious activities, and triaging the identified potentially suspicious activities including by determining an action to take with respect to at least a portion of the identified potentially suspicious activities based at least in part on the scoring. In various embodiments, a process for training a machine learning model to triage alerts includes configuring the machine learning model, and receiving training data. The process includes using computer processor(s) and the received training data to train the machine learning model to score potentially suspicious activities.